- Total grants
- Total funders
- Total recipients
- Earliest award date
- 29 Aug 2008
- Latest award date
- 17 Dec 2008
- Total GBP grants
- Total GBP awarded
- Largest GBP award
- Smallest GBP award
- Total Non-GBP grants
The key goals of SACORE are to develop and sustain a critical mass of local research scientists to be independent investigators and leaders capable of attracting competitive research grants. To achieve this we plan to improve research management and support capacity, introduce undergraduates to research, and stimulate research outputs at all levels of the research career pathway. This will be achieved through a number of interventions within the southern Africa region: 1. Developing south-south and strengthening existing north-south networks, through annual scientific meetings, mentorship, and joint PhD supervision. 2. Developing research leadership through mentorship of mid-level and senior scientists and by conducting relevant leadership and training courses. 3. Building a critical mass of research scientists, and research career pathways for them, through: a. competitive WT-SACORE PhD scholarships. b. competitive WT-SACORE post-doctoral fellowships. c. competitive WT-SACORE MSc scholarships d. providing competitive small research grants e. providing focus workshops to enhance research skills. 4. Consolidating career progression using professional development planning for all staff. 5. Establishing and strengthening Research Support Centres in the three low-income institutions including the provision of appropriate staff, equipment and skills and developing appropriate research governance and support frameworks. Importantly, the RSCs will pro-actively encourage and support scientists in their grant applications.
The World Health Organization and the social determinants of health: assessing theory, policy and practice (an international conference). 29 Aug 2008
The World Health Oraganisation and the Social Determinants of Health: Assessing theory, policy and practice (An international conference).
The 1918 influenza pandemic represents the worst outbreak of infectious disease in Britain in modern times. Although the virus swept the world in three waves between March 1918 and April 1919, in Britain the majority of the estimated 228,000 fatalities occurred in the autumn of 1918. In London alone deaths at the peak of the epidemic were 55.5 per 1,000- the highest since the 1849 cholera epidemic. Yet in the capital as in other great cities and towns throughout Britain, there was none of the panic that had accompanied earlier 19th century outbreaks of infectious disease at the heart of urban populations. Instead, the British response to the 'Spanish Lady' as the pandemic strain of flu was familiarly known was remarkably sanguine. As The Times commented at the height of the pandemic: 'Never since the Black Death has such a plague swept over the face of the world, [and] never, perhaps, has a plague been more stoically accepted.' The apparent absence of marked social responses to the 1918 influenza is a phenomenon much remarked on in the literature of the pandemic, as is the apparent paradox that despite the widespread morbidity and high mortality the pandemic had little apparent impact on public institutions and left few traces in public memory. However, to date no one has explored the deeper cultural 'narratives' that informed and conditioned these responses. Was Britain really a more stoical and robust nation in 1918, or was the absence of medical and other social responses a reflection of the particular social and political conditions that prevailed in Britain during the First World War and then medical nosologies and cultural perceptions of influenza? And if the 1918 pandemic was 'overshadowed,' as one writer puts it, by the war and the peace that followed the Armistice, what explains the similarly muted response to the Russian flu pandemic of the early 1890's, a disease outbreak that coincided with a long period of peace and stability in Britain? In this project I aim to show that, contrary to previous studies, both the 1918 and the 1889-92 Russian flu pandemic were the objects of much deeper public concern and anxiety than has previously been acknowledged and that the morbidity of prominent members of British society, coupled with the high mortality, occasioned widespread 'dread' and in some cases alarm. However, in 1918 at least, government departments and public institutions actively suppressed these concerns for the sake of the war effort and the maintenance of national morale.
Assessing the challenges faced by health systems in providing paediatric Cotrimoxazole prophylaxis in resource limited countries. 16 Sep 2008
HIV exposed infants are 16 times more likely to die in their second six months of life than unexposed infants, largely due to respiratory infections. Cotrimoxazole prophylaxis significantly reduces both mortality and morbidity. In Zimbabwe it is estimated that only 11% of exposed infants are prescribed cotrimoxazole and the extent to which it is correctly taken is unknown. The aim of the project is to describe the process and obstacles to provision of cotrimoxazole prophylaxis for HIV exposed infants in three sites in Zimbabwe, focusing on issues related to drug supply and adherence. This information will be used to develop an evidence-based intervention for improving its provision. The study will be conducted in three phases in two Zimbabwean health centres, one urban, one rural: 1) guidelines and standard operating procedures for PMTCT, cotrimoxazole prophylaxis and aftercare of HIV infected mothers will be studied; 2) implementation activities at study clinics including identification of HIV infected mothers, procedures for ensuring babies are prescribed cotrimoxazole and adhere to treatment will be assessed as will aftercare for HIV positive mothers; 3) the findings will be disseminated to stakeholders to identify solutions and develop an evidence based intervention relevant for Zimbabwe and the wider region
Modelling functional brain architecture. 16 Sep 2008
The aim of the proposed work is to create models that enable useful and informed inferences about brain function based upon whole-brain electromagnetic and hemodynamic responses. These models are important because they define the nature of the inferences made. Neuroimaging with electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) has an established role in nearly every aspect of cognitive, systems and clinical neuroscience. The data analysis procedures are now relatively sophisticated and ensure valid inferences. However, the statistical models employed are rather simple-minded and have little connection to neurophysiological procecesses, or the principles that might underlie brain function. Conventional models of fMRI are a little more sophisticated than most and rest on linear convolution modes of how changes in neuronal activity are expressed hemodynamically. However, even infMRI, interactions among different neuronal populations or cortical areas are precluded. This is important because the conceptual and biological validity of any forward model, of observed brain responses, places fundamental constraints on the validity and usefulness of inferences about that model. The programme of work described below is an attempt to finesse current models and ground them inneurophysiology and conceptual frameworks derived from machine learning. The hope is that inferences about the parameters of these models are empowered because the parameters have an explicit neurophysiological or mechanistic meaning. If successful, this work will facilitate two key ways of integrating theoretical ideas about large-scale brain function and experimental observations. The first rests on using brain responses to estimate physiologically meaningful parameters of neuronal architectures. The second approach uses data to disambiguate among competing models that embody key theoretical distinctions, formulated in terms of neurophysiology or machine learning; it is a relatively simple matter to identify the most likely model, given the data, using Bayesian model selection.